Problem statement: Gaze estimation systems compute the direction of eye gaze based on observed eye movements. The need for gaze-contingent applications is the basis of the current research work. The gaze pointing systems is a substitute for the existing input devices. Approach: The gaze tracking methods are either feature based or appearance based. In this study, an appearance based approach for gaze tracking is proposed based on Run Length Coding (RLC). The experiment was conducted considering transitional changes and the class-intervals in iris pixels. The image acquisition begins from the center of the screen in anticlockwise direction. The center of the screen was the pivot point. Results: Using RLC, the recognition rate of 95% was achieved. The image analysis in different directions determines the gaze point. The directions was determined with respect to the pivot point. Conclusion:The proposed system provides a robust, less computational gaze tracking method using web camera.

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The computation of gaze direction is important in modern interactive systems. The displays in real-time monitoring systems depend on spatial and temporal characteristics of eye movement. Research studies indicate the requirement for efficient and novel techniques in human computer interaction. A strong need for gaze tracking methods that eliminate initial setup and attune procedure is required. The pupil, iris and eye corners provide parametric data to determine gaze direction. Gaze tracking algorithm is initiated by iris localization. The approach of iris detection using frames captured from the video is significant for feature based gaze tracking. In this paper, the procedures for face and eye detection in visible light are discussed. The novel method discussed in this paper identify single face image appropriate for gaze tracking by elimination of multiple and non-face images. Iris detection is performed using Hough gradient method. The correctness rate of iris detection obtained is 95%.

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A system is proposed that considers minimal features using subpattern analysis which leads to less response time in a real time scenario. Using training samples, with a high degree of certainty, the minimum variance quadtree components [MVQC] of a signature for a person are listed to be applied on a testing sample. Initially the experiment was conducted on wavelet decomposed information for a signature. The non-MVQCs and core components were analyzed. To characterize the local details Gaussian-Hermite moment was applied. Later Hu moments were applied on the selected subsections. The summation values of the subsections are provided as feature to radial basis function [RBF] and feed forward neural network classifiers. Results indicate that the RBF classifier yielded 7% false rejection rate and feed forward neural network classification technique produced 9% false rejection rate. Promising results were achieved, by experimenting on the list of most prominent minimum variance components which are core components using RBF.

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